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Comparison of grey wolf optimizer (gwo) and ıncremental conductance (ınc) method for maximum power point tracking (mppt) in photovoltaic systems

Year 2023, Volume: 13 Issue: 4, 962 - 978, 15.10.2023

Ömer Öztürk Ömür Akyazı Bora Çavdar

https://doi.org/10.17714/gumusfenbil.1220816

Abstract

The need for energy in the world is increasing day by day with the advancements in technology. For this reason, there has been a trend towards renewable energy sources that are less harmful to the environment. Among renewable energy sources, solar energy (photovoltaic modules) is often preferred. The disadvantages of photovoltaic modules are that they negatively affect the stability (frequency, voltage) of electricity grids and have intermittent generation due to temperature, radiation and partial/full shading. This paper discusses maximum power point tracking (MPPT) in photovoltaic systems under partial shading conditions. In this study, the incremental conductance method (INC), one of the traditional techniques, and the gray wolf optimization (GWO) algorithm are compared in a Matlab/Simulink simulation environment. The output power efficiency of the system is 98.24% for GWO algorithm and 93.72% for INC method. The system output power is determined to have a settling time of 0.08 s and 0.18 s for the GWO algorithm and the INC method, respectively. These results demonstrate the success of GWO algorithm over INC method. Additionally, the study investigates how the change in the number of wolves (particle) in the GWO algorithm affects the output of the photovoltaic system. When the number of wolves is 3, 4, 5, the average output power values obtained are 2413 W, 2196.4 W, 1536.8 W, and 2349 W, respectively. When the number of wolves exceeds 3, oscillations in output power increase and efficiency decreases.

Keywords

Incremental conductance method Grey wolf optimizer Partial shading MPPT

References

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  • Almonacid, F. J. M. F., Rus, C., Hontoria, L., & Munoz, F. J. (2010). Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods. Renewable Energy, 35(5), 973-980. https://doi.org/10.1016/j.renene.2009.11.018
  • Altintaş, N., Yilmaz, A., DEMIRCI, A., & Tercan, S. M. (2021). Bataryalı PV sistemlerde maksimum güç noktası takip yöntemlerinin karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (21), 369-377. https://doi.org/10.31590/ejosat.702393
  • Azli, H., Titri, S., Larbes, C., Kaced, K., & Femmam, K. (2022). Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions. Solar Energy, 247, 295-307. https://doi.org/10.1016/j.solener.2022.10.029
  • Baba, A. O., Liu, G., & Chen, X. (2020). Classification and evaluation review of maximum power point tracking methods. Sustainable Futures, 2, 100020. https://doi.org/10.1016/j.sftr.2020.100020
  • Basha, C. H., Bansal, V., Rani, C., Brisilla, R. M., & Odofin, S. (2020). Development of cuckoo search MPPT algorithm for partially shaded solar PV SEPIC converter. In Soft Computing for Problem Solving, 1, 727-736. https://doi.org/10.1007/978-981-15-0035-0_59
  • Charin, C., Ishak, D., Zainuri, M. A. A. M., Ismail, B., & Jamil, M. K. M. (2021). A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions. Solar Energy, 217, 1-14. https://doi.org/10.1016/j.solener.2021.01.049
  • Chtita, S., Motahhir, S., El Hammoumi, A., Chouder, A., Benyoucef, A. S., El Ghzizal, A., ... & Askar, S. S. (2022). A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions. Scientific Reports, 12(1), 1-15. https://doi.org/10.1038/s41598-022-14733-6
  • Daraban, S., Petreus, D., & Morel, C. (2014). A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy, 74, 374-388. https://doi.org/10.1016/j.energy.2014.07.001
  • Demirtaş, M., İbrahim, S. E. F. A., Irmak, E., & Çolak, İ. (2008). Güneş enerjili sistemler için mikrodenetleyici tabanlı DA/DA yükselten dönüştürücü. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 23(3).
  • Eetivand, K., Zangeneh, A., & Nabavi, S. M. (2022). Hyper-Spherical search algorithm for maximum power point tracking of solar photovoltaic systems under partial shading conditions. International Transactions on Electrical Energy Systems, 2022. https://doi.org/10.1155/2022/1101692
  • Elshara, R. O. H. (2021). Parçacık sürü optimizasyonu yöntemine dayalı maksimum güç noktası izleme algoritmasının incelenmesi [Doktora Tezi, Kastamonu Üniversitesi Fen Bilimleri Enstitüsü].
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  • Kandemir, E. (2020). Kısmi gölgelenme koşullarında maksimum güç noktasında çalışan enerji geri kazanımlı tek dönüştürücülü şebeke bağlantılı PV sistem tasarımı ve uygulaması [Doktora Tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü]
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  • Kaysal, A., Köroğlu, S., Yüksel, O. Ğ. U. Z., & Kaysal, K. (2023). Kendinden ayarlı bulanık PI denetleyici tabanlı DA-DA dönüştürücü tasarımı ve deneysel uygulaması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(1), 483-496.https://doi.org/10.17341/gazimmfd.739775
  • Keskin, T. (2019). MPPT algoritmalarının gerçek zamanlı olarak karşılaştırılması ve PV sisteme uygulanması [Yüksek Lisans Tezi, Isparta Uygulamalı Bilimler Üniversitesi Lisansüstü Eğitim Enstitüsü].
  • Kulaksız, A., Gökkuş, G., & Alhajomar, F. (2019). Rapid control prototyping based on 32-Bit ARM Cortex-M3 microcontroller for photovoltaic MPPT algorithms. International Journal of Renewable Energy Research, 9(4), 1939-1947.
  • Mansoor, M., Mirza, A. F., & Ling, Q. (2020a). Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. Journal of Cleaner Production, 274, 122857. https://doi.org/10.1016/j.jclepro.2020.122857
  • Mansoor, M., Mirza, A. F., Ling, Q., & Javed, M. Y. (2020b). Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions. Solar Energy, 198, 499-518. https://doi.org/10.1016/j.solener.2020.01.070
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Mirza, A. F., Mansoor, M., Ling, Q., Yin, B., & Javed, M. Y. (2020). A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energy Conversion and Management, 209, 112625. https://doi.org/10.1016/j.enconman.2020.112625
  • Mohanty, S., Subudhi, B., & Ray, P. K. (2015). A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181-188. https://doi.org/10.1109/TSTE.2015.2482120
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  • Özdemir, A., & Pamuk, N. (2021). Kısmi gölgelenme şartları altındaki kompleks yapılı fotovoltaik enerji sistemlerinde maksimum güç noktası takibinin metasezgisel algoritmalar kullanılarak incelenmesi. Avrupa Bilim ve Teknoloji Dergisi, (31), 157-164. https://doi.org/10.31590/ejosat.1006248
  • Salman, S., Ai, X., & Wu, Z. (2018). Design of a P-&-O algorithm based MPPT charge controller for a stand-alone 200W PV system. Protection and Control of Modern Power Systems, 3(1), 1-8. https://doi.org/10.1186/s41601-018-0099-8
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Fotovoltaik sistemlerde gri kurt optimizasyon (gko) algoritması ve artımlı iletkenlik (ai) yönteminin maksimumum güç noktası takibi (mgnt) için karşılaştırılması

Year 2023, Volume: 13 Issue: 4, 962 - 978, 15.10.2023

Ömer Öztürk Ömür Akyazı Bora Çavdar

https://doi.org/10.17714/gumusfenbil.1220816

Abstract

Dünyadaki enerji ihtiyacı teknolojide yaşanan gelişmelerle gün geçtikçe artmaktadır. Bu sebeple, çevreye daha az zararlı olan yenilenebilir enerji kaynaklarına eğilim söz konusudur. Yenilenebilir enerji kaynakları arasında sıklıkla tercih edilen ise doğrudan güneş enerjisinin (fotovoltaik modüllerin) kullanımıdır. Fotovoltaik modüllerin dezavantajı elektrik şebekelerinde kararlılığı (frekans, gerilim) olumsuz etkilemesi ve sıcaklık, ışınım ve kısmi/tam gölgeleme gibi nedenlerle kesikli üretime sahip olmasıdır. Bu çalışmada kısmi gölgeleme koşulları altında fotovoltaik sistemlerde maksimum güç noktası takibi (MGNT) konusu ele alınmaktadır. Çalışmada geleneksel tekniklerden biri olan artımlı iletkenlik yöntemi (Aİ) ve gri kurt optimizasyon (GKO) algoritması Matlab/Simulink benzetim ortamında karşılaştırılmaktadır. Sistemin çıkış gücü veriminin GKO algoritması için 98,24% ve Aİ yöntemi için 93,72% olduğu elde edilmektedir. Sistem çıkış gücünün, GKO algoritması ve Aİ yöntemi için sırasıyla 0,08 s ve 0,18 s oturma zamanına sahip olduğu tespit edilmiştir. Bu sonuçlar, GKO algoritmasının Aİ yöntemine göre başarısını göstermektedir. Ayrıca çalışmada sistemin GKO algoritmasındaki kurt (parçacık) sayısı değişiminin fotovoltaik sisteminin çıkışını nasıl etkilediği incelenmektedir. Kurt sayısı 3, 4, 5 ve 6 olduğunda sırasıyla 2413, 2196,4, 1536,8 ve 2349 W ortalama çıkış gücü değerleri elde edilmektedir. Kurt sayısı 3’ü geçtiğinde çıkış gücünde salınımların arttığı ve verimin düştüğü gözlemlenmektedir.

Keywords

Artımlı iletkenlik yöntemi Gri kurt optimizasyon algoritması Kısmi gölgeleme MGNT

References

  • Ahmed, J., & Salam, Z. (2015). An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy, 150, 97-108. https://doi.org/10.1016/j.apenergy.2015.04.006
  • Almonacid, F. J. M. F., Rus, C., Hontoria, L., & Munoz, F. J. (2010). Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods. Renewable Energy, 35(5), 973-980. https://doi.org/10.1016/j.renene.2009.11.018
  • Altintaş, N., Yilmaz, A., DEMIRCI, A., & Tercan, S. M. (2021). Bataryalı PV sistemlerde maksimum güç noktası takip yöntemlerinin karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (21), 369-377. https://doi.org/10.31590/ejosat.702393
  • Azli, H., Titri, S., Larbes, C., Kaced, K., & Femmam, K. (2022). Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions. Solar Energy, 247, 295-307. https://doi.org/10.1016/j.solener.2022.10.029
  • Baba, A. O., Liu, G., & Chen, X. (2020). Classification and evaluation review of maximum power point tracking methods. Sustainable Futures, 2, 100020. https://doi.org/10.1016/j.sftr.2020.100020
  • Basha, C. H., Bansal, V., Rani, C., Brisilla, R. M., & Odofin, S. (2020). Development of cuckoo search MPPT algorithm for partially shaded solar PV SEPIC converter. In Soft Computing for Problem Solving, 1, 727-736. https://doi.org/10.1007/978-981-15-0035-0_59
  • Charin, C., Ishak, D., Zainuri, M. A. A. M., Ismail, B., & Jamil, M. K. M. (2021). A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions. Solar Energy, 217, 1-14. https://doi.org/10.1016/j.solener.2021.01.049
  • Chtita, S., Motahhir, S., El Hammoumi, A., Chouder, A., Benyoucef, A. S., El Ghzizal, A., ... & Askar, S. S. (2022). A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions. Scientific Reports, 12(1), 1-15. https://doi.org/10.1038/s41598-022-14733-6
  • Daraban, S., Petreus, D., & Morel, C. (2014). A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy, 74, 374-388. https://doi.org/10.1016/j.energy.2014.07.001
  • Demirtaş, M., İbrahim, S. E. F. A., Irmak, E., & Çolak, İ. (2008). Güneş enerjili sistemler için mikrodenetleyici tabanlı DA/DA yükselten dönüştürücü. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 23(3).
  • Eetivand, K., Zangeneh, A., & Nabavi, S. M. (2022). Hyper-Spherical search algorithm for maximum power point tracking of solar photovoltaic systems under partial shading conditions. International Transactions on Electrical Energy Systems, 2022. https://doi.org/10.1155/2022/1101692
  • Elshara, R. O. H. (2021). Parçacık sürü optimizasyonu yöntemine dayalı maksimum güç noktası izleme algoritmasının incelenmesi [Doktora Tezi, Kastamonu Üniversitesi Fen Bilimleri Enstitüsü].
  • Fan, L., & Ma, X. (2022). Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control. Scientific Reports, 12(1), 1-12. https://doi.org/10.1038/s41598-022-08327-5
  • Gümüş, Z., & Demirtaş, M. (2021). Fotovoltaik sistemlerde maksimum güç noktası takibinde kullanılan algoritmaların kısmi gölgeleme koşulları altında karşılaştırılması. Politeknik Dergisi, 1-1. https://doi.org/10.2339/politeknik.725255
  • Hussein, K. H., Muta, I., Hoshino, T., & Osakada, M. (1995). Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. IEE Proceedings-Generation, Transmission and Distribution, 142(1), 59-64. https://doi.org/10.1049/ip-gtd:19951577
  • Javed, M. Y., Hasan, A., Rizvi, S. T. H., Hafeez, A., Sarwar, S., & Telmoudi, A. J. (2022). Water cycle algorithm (WCA): A new technique to harvest maximum power from PV. Cybernetics and Systems, 53(1), 80-102. https://doi.org/10.1080/01969722.2021.2008683
  • Kandemir, E. (2020). Kısmi gölgelenme koşullarında maksimum güç noktasında çalışan enerji geri kazanımlı tek dönüştürücülü şebeke bağlantılı PV sistem tasarımı ve uygulaması [Doktora Tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü]
  • Karagöz, M. K. (2021). Design and implementation of the BAT algorithm based maximum power point tracker that able to manage partial shadow conditions for PV systems [Doktora Tezi, Karabük Üniversitesi Eğitim Enstitüsü]
  • Kaysal, A., Köroğlu, S., Yüksel, O. Ğ. U. Z., & Kaysal, K. (2023). Kendinden ayarlı bulanık PI denetleyici tabanlı DA-DA dönüştürücü tasarımı ve deneysel uygulaması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(1), 483-496.https://doi.org/10.17341/gazimmfd.739775
  • Keskin, T. (2019). MPPT algoritmalarının gerçek zamanlı olarak karşılaştırılması ve PV sisteme uygulanması [Yüksek Lisans Tezi, Isparta Uygulamalı Bilimler Üniversitesi Lisansüstü Eğitim Enstitüsü].
  • Kulaksız, A., Gökkuş, G., & Alhajomar, F. (2019). Rapid control prototyping based on 32-Bit ARM Cortex-M3 microcontroller for photovoltaic MPPT algorithms. International Journal of Renewable Energy Research, 9(4), 1939-1947.
  • Mansoor, M., Mirza, A. F., & Ling, Q. (2020a). Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. Journal of Cleaner Production, 274, 122857. https://doi.org/10.1016/j.jclepro.2020.122857
  • Mansoor, M., Mirza, A. F., Ling, Q., & Javed, M. Y. (2020b). Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions. Solar Energy, 198, 499-518. https://doi.org/10.1016/j.solener.2020.01.070
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Mirza, A. F., Mansoor, M., Ling, Q., Yin, B., & Javed, M. Y. (2020). A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energy Conversion and Management, 209, 112625. https://doi.org/10.1016/j.enconman.2020.112625
  • Mohanty, S., Subudhi, B., & Ray, P. K. (2015). A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181-188. https://doi.org/10.1109/TSTE.2015.2482120
  • Murdock, H. E., Gibb, D., André, T., Sawin, J. L., Brown, A., Ranalder, L., ... & Brumer, L. (2021). Renewables 2021-Global status report. https://inis.iaea.org/search/search.aspx?orig_q=RN:52059346y
  • Nusaif, A. I., & Mahmood, A. L. (2020). MPPT algorithms (PSO, FA, and MFA) for PV system under partial shading condition, Case study: BTS in Algazalia, Baghdad. International Journal of Smart Grid-ijSmartGrid, 4(3), 100-110.
  • Özdemir, A., & Pamuk, N. (2021). Kısmi gölgelenme şartları altındaki kompleks yapılı fotovoltaik enerji sistemlerinde maksimum güç noktası takibinin metasezgisel algoritmalar kullanılarak incelenmesi. Avrupa Bilim ve Teknoloji Dergisi, (31), 157-164. https://doi.org/10.31590/ejosat.1006248
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There are 43 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Ömer Öztürk 0000-0001-7403-5343

Ömür Akyazı 0000-0001-6266-2323

Bora Çavdar 0000-0002-0545-2925

Publication Date October 15, 2023
Submission Date December 18, 2022
Acceptance Date September 2, 2023
Published in Issue Year 2023 Volume: 13 Issue: 4

Cite

APA Öztürk, Ö., Akyazı, Ö., & Çavdar, B. (2023). Fotovoltaik sistemlerde gri kurt optimizasyon (gko) algoritması ve artımlı iletkenlik (ai) yönteminin maksimumum güç noktası takibi (mgnt) için karşılaştırılması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(4), 962-978. https://doi.org/10.17714/gumusfenbil.1220816
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